The typical AWS bill, otherwise known as the AWS Cost and Usage Report, includes line items that are useful to both finance and DevOps. However, many of the metrics that are within engineers’ and cloud architects’ control aren’t so simple to discover. To make cost a first-class operational metric for DevOps, teams need visibility into the data that’s relevant to engineering activity.

Tracing is one of the key tools that Honeycomb offers to make sense of data. Over the last few weeks, we’ve made a number of improvements to our tracing interface — and, put together, those changes let you think about traces in a whole new way! Tracing makes it easier to understand control flow within a distributed system. We render traces with waterfall diagrams, which capture the execution history of individual requests.

Unlike traditional SQL databases, NoSQL databases, or “non-SQL” databases, do not store their data in tabular relations. Originally designed for modern web-scale databases, they have found widespread use in present-day big data and real-time web applications. Some of the most commonly used data structures include key-value, wide column, graph, and document stores.

Metrics for all – and all for metrics. At Grafana, we not only strive to give people a “single pane of glass” to unify observability metrics. From the very start, our mission has been to advocate for the democratization of metrics, which is the idea that the paradigm needs to shift between who can store data, why they need to store it, and, ultimately, what they’re able to with it. And Grafana users are a great example of how vast and varied the needs are for data access.

Atlassian is built on the belief that there are better ways to get work done. If we find a way of working to be ineffective, we will change it. This belief is fundamental to how we build and sell our products, and now we’re applying it to how we acquire companies (aka mergers and acquisitions, or “M&A”).

After our initial release of Icinga Reporting for early adopters we continued our development and are happy to release v0.9.1 today. The release includes bug fixes and some minor enhancements for the usability. I want to take this opportunity to write a post aimed at the people who are new to the whole reporting shabang – like me. This week I set out to figuring out for myself why one would need to use reporting and how to get there – and to share my new knowledge with you!

AWS CloudWatch Logs gives you full visibility into your AWS infrastructure, from individual workloads to the services that bind them. Monitoring these logs helps ensure their smooth and continued operation, ongoing stability, and performance. Integrating CloudWatch Logs with LogDNA makes it easier to parse, search, and analyze AWS logs in order to detect anomalies and troubleshoot problems faster.

There was time, where CRT screens were ubiquitous. In this time dark websites were pretty common since the way CRT screen emits light worked pretty well with dark colours. Then LCD screens came into every hour. Those were pretty bad compared to CRT, especially in terms of showing true shades of grey. This was the end of black websites popularity. Dark went underground.

Whether you’re investigating memory leaks or debugging errors, Java Virtual Machine (JVM) runtime metrics provide detailed context for troubleshooting application performance issues. For example, if you see a spike in application latency, correlating request traces with Java runtime metrics can help you determine if the bottleneck is the JVM (e.g., inefficient garbage collection) or a code-level issue.

Serverless first came onto the scene in 2014 when AWS Lambda was launched. It offers a dynamic cloud-computing execution model where the server is run by the cloud provider. As with any relatively recent technology, its novelty results in a steep learning curve, and it comes with its own set of benefits and drawbacks.